1081results about "Web data querying" patented technology

The invention relates to an industrial equipment maintenance intelligent question-answering system based on multi-agent collaboration. Wherein the input unit is used for receiving text, voice, image or equipment scanning and other multi-mode user input information and analyzing the information into structured problem information; the scheduling unit performs semantic understanding and problem classification on the structured problem information based on the fine-tuned cross-language pre-training language model and an incremental training mechanism; the processing unit calls a corresponding domain agent according to the classification result, and generates an intelligent question and answer processing result including predictive maintenance suggestions, structured reply content and semantic annotation information; and the fusion unit fuses the local knowledge base, the graph database and the networking retrieval information, performs multi-hop semantic reasoning on the intelligent question and answer processing result, and generates multi-modal reply information including text description, image screenshots, prediction curves and recommendation links. The system can support multi-language and multi-mode intelligent question answering and predictive maintenance in a complex industrial maintenance scene.

The invention discloses a public opinion risk dynamic early-warning method and system based on a large language model, and the method and system carry out the semantic recognition, event extraction and situational reasoning of unstructured public opinion data through introducing an advanced large language model, and carry out the dynamic early-warning of the public opinion risk in combination with structured emotion indexes and market dynamics. Potential risk factors in social public opinions can be automatically identified, and risk quantitative evaluation is realized in combination with semantic tags, market behavior data and emotion intensity. The system has strong prospective perception ability and event evolution reasoning ability, can provide rapid and accurate early warning information, and further can generate personalized suggestions and decisions in combination with user risk preferences. The method can be widely applied to a plurality of public opinion sensitive fields such as enterprise brand management, government supervision, financial institutions and media transmission control, and the automation and precision capability of dealing with sudden public opinion risks is effectively improved.

The invention discloses an enterprise exhaustion report intelligent generation method and system based on a large model, and relates to the technical field of artificial intelligence application. The demand understanding module analyzes a complete call demand and a key field in a text input by a user, and presets a template of a complete call report; the intelligent reasoning module constructs execution plans of other modules according to user exhaustion demands and report task characteristics, and formulates report task targets and output standards; the multi-source data scheduling module is in butt joint with a structured database of each administrative department related to an enterprise, and converts a natural language query requirement into an SQL statement capable of directly calling the database; the public opinion analysis module retrieves related news public opinions and performs emotional tendency analysis by using a large model; the intelligent integration module carries out integration according to a preset template of the exhaustion call report to give a comprehensive conclusion; the interactive generation module performs detail adjustment on the exhaustion report based on the real-time feedback of the user to obtain a final exhaustion report; the working efficiency is remarkably improved.

The invention relates to the technical field of information retrieval, in particular to a vertical website deep crawling method based on intention recognition, which comprises the following steps: crawling data from a plurality of heterogeneous information sources, and constructing a dynamic knowledge graph; identifying a weak signal association path formed by connecting a plurality of relation edges in the dynamic knowledge graph, and converting the weak signal association path into a to-be-verified risk hypothesis; generating a data acquisition intention, and based on the to-be-verified risk hypothesis, generating a rejection query for searching reverse evidence as a first data acquisition intention; receiving an input query instruction, and performing semantic analysis on the query instruction to generate a second data acquisition intention; a multi-source crawling strategy is generated based on the type of the data collection intention, and the multi-source crawling strategy comprises a plurality of vertical website sources and crawling priorities of the vertical website sources; executing a deep crawling task according to the multi-source crawling strategy to obtain target data; and processing the target data, verifying the to-be-verified risk hypothesis, and judging whether the reverse evidence exists or not and judging the intensity of the reverse evidence.

A text processing method includes obtaining a query text, invoking a search engine interface based on the query text to obtain a plurality of text search results corresponding to the query text, obtaining, from the plurality of text search results, a plurality of answer text segments matching the query text, determining a relevance between the query text and each of the plurality of answer text segments, determining one of the plurality of answer text segments that corresponds to a maximum relevance as a reference text of the query text, and invoking a language model based on the query text and the reference text to obtain a reply text of the query text.

An RAG auxiliary generation type search engine method based on multiple rounds of expert and self-adaptive mixed retrieval comprises the steps that a crawler is used for crawling data, the crawled data are processed, a graph knowledge base is constructed through text partitioning and entity relation extraction, the graph knowledge base is coded into semantic vectors, and a vector database is established; by calculating similarity between subject vectors and literature vectors in a vector database, retrieving related literatures, utilizing a large language model to simulate a multi-expert role to generate differentiated viewpoints, and combining multiple rounds of dialogues and user feedback, generating a multi-view fused article to complete multiple rounds of expert answers; designing a double-layer retrieval mechanism according to different nodes and relationships in the retrieval atlas based on the atlas knowledge base; meanwhile, dense retrieval of a vector knowledge base of text coding is combined and utilized, a map-vector collaborative retrieval mechanism is designed, a self-adaptive mechanism and a session memory function are introduced, and multi-round question and answer response is carried out; according to the method and the system, higher logical contents can be generated.

The invention provides a service recommendation decision-making method based on large model retrieval enhancement generation and related equipment. The method comprises the following steps: acquiring an unstructured service demand; calling a large language model to perform semantic analysis and extension on the service demand, and generating an unstructured extended semantic text; encoding the extended semantic text to obtain a semantic query vector; querying a service description of each pre-stored service from a service database, respectively calculating a first similarity score between the semantic query vector and the service description, expanding a second similarity score between each keyword in the semantic text and the service description, and screening out a candidate service set having semantic association with the service demand; all corresponding application programming interfaces in the candidate service set are determined, and a final application programming interface set is screened out and recommended according to the use frequency of the various application programming interfaces, the semantic similarity among the labels and the semantic query vectors. According to the method and the device, the API recommendation accuracy is improved.

The invention relates to artificial intelligence security, in particular to a large model security alignment method based on intention reasoning and RAG guidance, which comprises the following steps: 1, performing structured enhancement on original input before large model reasoning, including security criterion introduction and prison break strategy matching; 2, on the basis of input enhancement, in combination with thinking chain reasoning and a multi-path sampling mechanism, fine-grained classification is carried out on user intentions, and illusion and misjudgment in the reasoning process are reduced; and 3, after the large model generates a response, performing multi-dimensional security assessment on the output by means of an external assessment tool, and guiding the large model to perform iterative optimization based on feedback to form a'generation-assessment-correction 'closed-loop mechanism, the method effectively resists prison break attacks, corrects the inherent defects of the large model, such as illusion, prejudice and the like, and improves the security of the large model. And by displaying the reasoning path and the improvement process, the interpretability of large model decision is enhanced, the general compliance constraint is broken through, and the precise compliance guarantee of high-sensitivity scenes such as medical treatment and finance is realized.

A processor-implemented method including separating a first document into a second document, the second document including a first metadata portion of first metadata of the first document, and a third document, the third document including a first content portion of content of the first document, classifying the second document and the third document into a first material set and a second material set, respectively, and indexing the second document and the third document according to a correlation of the second document and the third document.

The invention relates to the technical field of natural language processing, and discloses a question retrieval method and system, electronic equipment and a storage medium. The method comprises the steps of performing semantic understanding and decomposition processing on an original question to obtain a plurality of candidate sub-questions; constructing an initial problem tree structure by taking the original problem as a root node and the candidate sub-problems as sub-nodes; rationality scores of the child nodes are determined, and the rationality scores represent rationality of candidate sub-problems corresponding to the child nodes; determining a relevance score between the nodes, the relevance score representing a semantic repetition condition between the nodes; based on the rationality score and the relevance score, optimizing the initial problem tree structure to obtain a target problem tree structure; and performing retrieval based on the target question tree structure to obtain answer data for the original question. By means of the method, rationality and relevance evaluation can be conducted on the problem tree nodes, and architectural disassembly and semantic hierarchical management of complex problems are achieved.

Techniques for implementing a scalable automated training framework for anomaly and ransomware detection are disclosed. In some embodiments, a computer system performs operations comprising: instantiating a plurality of virtual machines, each one of the virtual machines being loaded with a corresponding file system; simulating user actions and ransomware on the virtual machines, the simulating of user actions and ransomware on the virtual machines causing changes to the corresponding file systems of the virtual machines; for each one of the plurality of virtual machines, generating a corresponding metadata file based on one or more corresponding snapshots of the virtual machine, the one or more corresponding snapshots indicating the changes to the corresponding file system of the virtual machine; and training a ransomware detection model using a machine learning algorithm and training data, the training data being based on the corresponding metadata files of the virtual machines.

The invention discloses a multi-modal data retrieval method and device under dynamic collaboration and a medium, belongs to the technical field of information retrieval and natural language processing, and is used for solving the problems that unstructured data is difficult to process efficiently in traditional data retrieval, an information island phenomenon exists, and the data retrieval efficiency is low. And an effective connection and integration mechanism is lacked between data from different sources. The method comprises the following steps: performing task allocation processing on a corresponding Agent cluster on a question type to be queried to obtain an Agent task list; performing data query processing on the Agent task list under different data sources, and obtaining query result data; performing data primary processing on the query result data to determine initial retrieval data; performing multi-modal consistency check on the initial retrieval data, and integrating verified integrated retrieval data; and performing information strategy generation processing based on a fact verification mode and an explanation enhancement mode on the integrated retrieval data to obtain final answer information.

The invention discloses a domain name resource record TTL tampering traceability positioning method based on analytic chain reasoning, and belongs to the technical field of Internet security monitoring. The method comprises the following steps: deploying a system which comprises a special server for managing a domain name, a plurality of repeaters and a plurality of recursive resolvers located at the upstream of the repeaters, and setting a TTL reference value stored in the special server; selecting a target transponder, and initiating a plurality of DNS query requests with time intervals to the target transponder by a user; and determining a cache mode of the target transponder based on the TTL value in the response corresponding to each DNS query request, and determining an object tampering the TTL reference value based on the cache mode of the target transponder and the TTL value in the response corresponding to each DNS query request. The method is used for realizing hierarchical positioning of TTL tampering responsibilities.

Systems and methods for retrieving information from a corpus of documents that is relevant to a query. The method comprises: generating a first plurality of chunks by subdividing each document in the corpus of documents into one or more chunks of a first size; generating a second plurality of chunks by subdividing each document in the corpus of documents into one or more chunks of a second, larger, size; using an information retrieval system to identify, from the second plurality of chunks, a set of chunks of the second size that are relevant to a query; and using the information retrieval system to identify, from a subset of chunks of the first plurality of chunks, a set of chunks of the first size that are relevant to the query. The subset is based on the set of chunks of the second size that are relevant to the query.

The invention discloses a black-gray production control agent training generation and processing method and device based on a large model, a medium and equipment. The invention belongs to the technical field of computers. The method comprises the steps of intelligent agent training: training task understanding ability, task planning ability, subtask execution ability and tool calling ability of an intelligent agent through a research and judgment report document and a settlement file document of historical case events related to black and grey production management to obtain a black and grey production management intelligent agent; collection of case event data: acquiring various data related to the current case event in a synchronous or asynchronous, real-time or non-real-time manner through a retrieval enhancement method; and intelligent agent execution: calling related tools to execute each subtask, and carrying out parameter transmission and fusion calculation to obtain an output result of the black and grey production control intelligent agent. By adopting the technical scheme, the efficiency and the accuracy of black ash production treatment can be improved, and the capability of coping with complicated and diversified black ash production problems is enhanced.

Systems and methods for machine learning-informed response and augmentation of incomplete queries and query artifacts that include executing parallel search engines in a machine learning pipeline based on an input of a query to retrieve one or more corpora of candidate digital artifacts, constructing a merged corpus of candidate digital artifacts based on the retrieved corpora of digital artifacts, ranking each candidate digital artifact of the merged corpus of candidate digital artifacts based on a computed relevance to the input query, and returning, via a user interface, a response to the input query based on a subset of prioritized candidate digital artifacts most relevant to the input query.

The invention relates to the technical field of artificial intelligence, in particular to a multi-modal large model-based bidding and cross-bidding early warning and blocking method and system, and the method comprises the following steps: receiving and preprocessing various qualification files, including business licenses, industry licenses, auditing reports and bidding files, submitted by suppliers; applying a document layout analysis model based on joint training of a visual coding network and a text alignment network; the method has the beneficial effects that a scanned certificate, an audit report and a bidding file are automatically analyzed into structured semantic fragments; performing same semantic space alignment on the fragments, supplier basic information, historical records and real-time captured data of an authoritative authentication website; any contradiction or gap among different sources of key information such as registration capital, permission items, financial indexes and the like can be quickly found, and interpretable risk scores and processing suggestions can be given; on the premise of ensuring the private security of the data, the qualification auditing efficiency and accuracy are improved, and the workload of manual checking is reduced.

The invention provides an RAG adaptive evidence refining method based on submodule optimization, and belongs to the technical field of artificial intelligence. Collecting data to construct a knowledge base, and generating a candidate document set according to the knowledge base inquired and retrieved by a user; designing an instruction and calling a DeepSeek model to evaluate the answer contribution degree of the candidate document, calculating the relevancy between the query and the candidate document and the information coverage degree score of the optimal document to the candidate document, and constructing a sub-module objective function; calculating a marginal gain threshold value, iteratively calculating optimal documents with marginal gains greater than the threshold value, and generating an optimal document set; and splitting the optimal document set according to the paragraph level, generating a candidate paragraph set, calculating the redundancy score of the optimal paragraph set, constructing a sub-module objective function added with redundancy penalty by multiplexing the method, and performing iterative calculation to generate a final optimal evidence paragraph subset. The method effectively solves the problems of over-high redundancy, over-low information coverage and the like caused by a traditional processing method, so that the final model reasoning performance is improved.

Embodiments of the present application provide a task execution method and system, an electronic device, and a computer storage medium. A task execution method comprises: during the process of executing a program code by means of a code execution environment, detecting an execution trigger code for a preset task; according to the execution trigger code, performing scheduling strategy querying by means of the code execution environment to obtain a scheduling strategy for the preset task; and scheduling, via an environment communication interface of the code execution environment, the preset task to be executed in a code execution environment of a cloud target device indicated by the obtained scheduling strategy. By means of embodiments of the present application, the execution speed and efficiency of the preset task are improved, and the running speed of the client terminal is also improved, thereby optimizing the performance of the client terminal.

Various embodiments improve search technologies and computer information retrieval by executing a query via ranking a set of search result candidates higher than another set search result candidates based at least in part on the query and determining that a first set of search result candidates are indicative of a sub-accessory to an accessory or an accessory itself.

The invention discloses a table data analysis method fusing a large language model and a ReAct framework. The table data analysis method comprises the following steps: receiving and processing table data and natural language query input by a user through a file analysis module; performing task analysis and code generation in the ReAct framework by using an LLM decision engine; constructing a multi-stage verification module to perform three-stage verification of semantic calibration, code pre-check and logic verification on the generated code to enhance the code robustness and improve the result accuracy; a CNN-BiLSTM hybrid neural network model is adopted to construct a self-correction module, error classification is carried out on codes which fail to be verified, and a correction strategy is fed back; and executing the generated code, and returning a structured result after the ReAct control process is finished. According to the method, the problems that error propagation is not corrected, multi-Agent resource consumption is large and complex query accuracy is low in an existing table analysis technology are effectively solved, the accuracy, efficiency and reliability of data analysis are remarkably improved, and meanwhile computing resource consumption is greatly reduced.

Provided in the present disclosure are a commodity object search method and apparatus, and an electronic device and a storage medium. According to the embodiments of the present disclosure, on the basis of a search request triggered on the basis of a client, search basis data for searching for a commodity object may be acquired first; then, on the basis of the search basis data, a first prompt word corresponding to a commodity object search scenario is generated, and interaction with a large language model is performed by means of the first prompt word, so as to obtain search intent information corresponding to the current commodity object search; and finally, on the basis of the search basis data and the search intent information, a second prompt word corresponding to the commodity object search scenario is generated, and interaction with the large language model is performed by means of the second prompt word, so as to obtain a search result corresponding to the current commodity object search. By using the solution, interaction can be performed with a large language model, so as to deeply analyze and understand a search intent of a client user in searching for a commodity object, and provide a more accurate commodity object recommendation scheme on the basis of the search intent.

The invention relates to an e-commerce search service recovery method and device, equipment and a medium, and the method comprises the steps: starting a local search service deployed by a current fault node, and sending a missing data request to a remote search service of any healthy node, the request comprising a latest timestamp of a latest log record in a local transaction log library, the healthy nodes and the fault nodes are disaster recovery nodes; receiving a complete transaction log file which is returned by the remote search service and is generated after the latest timestamp, grouping log records in the complete transaction log file according to the commodity identifier, and determining a target log record with the maximum global version number in each group; constructing the target log record into an incremental transaction log file, writing the incremental transaction log file into a local transaction log library, and replaying the incremental transaction log file into search engine index data; and after the playback is completed, the local search service is registered to the service registration center again. According to the method, the fault node can be quickly recovered, and high consistency of the commodity data among the search service nodes is ensured.

The invention relates to an automatic driving simulation test system based on a local cluster. According to the system, a local structured file is used as a database, a plurality of common computers are built into a distributed computing cluster through an SSH protocol, and a Web front end is integrated to provide a user interface. The core executes a priority scheduling algorithm fused with a dynamic adjustment and preemption mechanism through an intelligent scheduling and control module, and drives a simulation software API (Application Program Interface) to realize full-process automation from task submission, scheduling, distributed parallel execution, state monitoring to result collection and report generation. The invention further relates to a corresponding method and device, a processor and a computer readable storage medium. According to the automatic driving simulation test system, method and device based on the local cluster, the processor and the storage medium, the problems that an existing simulation test cloud platform is high in cost and complex in deployment are effectively solved, and an efficient, low-cost and high-flexibility test solution is provided for automatic driving research and development.

The embodiment of the invention provides a large language model optimization method based on variance reduction and momentum acceleration. The method comprises the following steps: a gradient estimation stage of a large language model: initializing a seed list and a projection list; executing multiple independent query iterations, calling a disturbance subprogram, generating a random seed for the large language model, and storing a table; in the disturbance subprogram, the determined gradient projection value is stored in a projection list, and iteration is carried out for next query; after executing multiple independent query iterations, storing a plurality of random seeds and a plurality of gradient projection values corresponding to the random seeds; in the weight updating stage of the large language model, a gradient norm subprogram is called for each layer of the large language model, and a random seed is obtained to reset a random number generator; and determining variance-reduced gradient estimation according to the gradient projection value extracted from the projection list and the reproduced disturbance vector. According to the embodiment of the invention, gradient information queried for multiple times is aggregated to generate low-noise gradient estimation, and fine tuning of a large language model is completed.

The invention relates to a challenge authentication mechanism-based verifiable on-chain data query method, and belongs to the technical field of block chains. According to the method, the maintenance task of the authentication data structure ADS is entrusted to a limited number of service providers from a full node, and the correctness of the maintenance task is ensured by the full node through verification instead of reconstructing the ADS. A mechanism based on challenge authentication is designed, and a service provider extracts on-chain data, constructs an ADS and provides a query service; before a user queries, a challenge node constructs a challenge token containing a known result query expression and sends the challenge token to a service provider, and after the service provider issues a query result and a proof, the result correctness and the proof validity are verified in a full-node manner, so that an attack initiated by a wrong ADS can be detected at an extremely high probability. According to the method, the ADS maintenance overhead is greatly reduced while the correctness of the ADS is ensured, the validity of verifiable query is ensured, and the method has very strong robustness and very high security for adaptive attacks, so that the diversity of query services is effectively expanded.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes such as financial science and technology, medical health and the like, and discloses an abnormity early warning method, device, equipment and medium based on stage penetration, and the method comprises the steps: collecting interaction whole-process behaviors to form a continuous data chain, and constructing a multi-dimensional dynamic portrait; establishing a stage penetration model, and defining progressive migration of three stages of service interaction, service use and exception occurrence; calculating the overlap ratio of the interactive and used feature identifiers at the first level, and calculating the difference degree of the used and abnormal feature identifiers at the second level; training an early warning model in combination with historical data; inputting the current state of the portrait, and outputting an abnormal level; and triggering an early warning signal according to the grade. According to the method, portraits and historical statistics are converted into calculable coincidence degree and difference degree indexes through staged modeling and hierarchical penetration, and the early warning model is formed by weights and threshold values, so that the abnormal level is judged in advance, the recognition accuracy and the response timeliness are improved, and preferential disposal of resources is facilitated.

The invention provides a method and a system for performing information association review based on RPA and AI. The method comprises the following steps: an RPA client automatically controls a browser to log in an enterprise query website; the RPA client reads the supplier file, inputs supplier information into an enterprise query website for query, and stores basic information of each supplier to the supplier file; the RPA client calls an enterprise association relationship query function of an enterprise query website, and stores a supplier association relationship picture to the local; the RPA client calls the multi-modal model through an interface and identifies relation picture information; the RPA client sends the correctly identified information and the supplier basic information to a large language model, checks whether a preset association rule is triggered or not, and outputs a result; and the RPA client writes the review result into a local file and sends the review result to a designated person for review. According to the invention, the examination efficiency, accuracy and data integration capability of information association examination can be improved.

The invention belongs to the technical field of information, and particularly relates to an event co-reference resolution method based on multiple agents. The method specifically comprises the following steps: splicing prompt words of an event attribute recognition agent with a chapter text to be subjected to event co-reference analysis, and then inputting the event attribute recognition agent; the event attribute recognition agent follows the input content output event type and role or the output is null; when the output of the event attribute recognition agent is null, performing question query on the exogenous information integration agent; after a return result of the exogenous information integration agent is added, the event attribute identification agent generates a corresponding event type and role again; splicing the event type, the role and the chapter text, and inputting an event co-reference prediction agent; and the event co-reference prediction agent realizes event co-reference resolution for each event mention according to the sequence.